We have
released support for Azure Document DB as a data source in Azure Machine
Learning. You can use the existing "Azure DocumentDB" connection
option in the Import Data module to read data from Azure DocumentDB for your experiment.
For more information, please see the DocumentDB
section of the Import Data module.

You can use this module to extract key talking points from text. As an input, the module takes a dataset that must have a text string column from which the key-phrases are extracted.

The module takes the language of the text records as input parameter. Supported languages include Dutch, English, French, German, Italian and Spanish. You can also use a language column that specifies the language of each record, as produced by Detect Languages module.

The output consists of comma-separated lists of key phrases for each record in input. The key phrases can be used to summarize a corpus of documents, or as features for a machine learning model.

Updated Module: Preprocess Text

You can specify a language through a language column, as produced by Detect Languages module.

Following three preprocessing options have been added: Expand verb contractions, Normalize backslashes to slashes, and Split tokens on special characters. Previously, these transformations were done automatically.

We are pleased to announce the availability of Azure Machine Learning Workspaces and Web Service Plans for all our Azure Machine Learning users through the Azure Portal. Azure Machine Learning users can now create and manage Standard workspaces through the Azure Portal. In addition, users will also be able to create Web Service Pricing Plans. These plans are used when deploying web services and provide included quantities of operationalized compute at a single, predictable monthly cost.

Create your Standard Azure Machine Learning workspace now by going to https://portal.azure.com. Log in with the credentials that you use for accessing your Azure Subscription(s). Click on +New | Data + Analytics | Machine Learning Workspace.

These modules allow you to build models to solve text classification problems, such as support ticket routing or sentiment analysis. You can pre-process text in multiple languages, and then create features from your text data. Operationalization of models is fully supported.

The modules complement the existing capabilities for Feature Hashing, Vowpal Wabbit based high-dimensional models, and text analytics through R and Python scripting.

There is an issue impacting the "New" web service option for deploying web services from Predictive Experiments in Azure ML. We are working on resolving the issue, and a result have disabled the feature until the feature is fully functional. To access web services created the new process, please browse to https://services.azureml.net and sign in to view your web services. Sorry for any inconvenience this issue may cause.

We have released support for Azure SQL Data
Warehouse as a data source and a destination in Azure Machine Learning. You can
use the existing "Azure SQL Database" connection options in the
Reader and Writer modules to read from and write to Azure SQL Data Warehouse.
When using the Writer module, the destination tables must already exist in the
SQL Data Warehouse.

Visualization of tree models such as Boosted Decision Trees is now available in Azure Machine Learning Studio. To view the trees, train the model, and click Visualize on the output of Train Model module.

Announcing the Availability of an Azure Virtual Machine Image with Popular Data Science Tools

Microsoft Data Group is happy to announce the immediate availability of a Windows Server 2012 based custom virtual machine image on the Azure marketplace containing several tools that can be used by data scientists and developers for advanced analytics. Through Azure’s world-wide cloud infrastructure, customers now have on-demand access to a data science development environment they can use to derive insights from their data, build predictive models and intelligent applications.The virtual machine saves developers’ time from having to discover and install the tools individually.Hosting the data science machine on Azure gains you high availability and a consistent set of tools used across your data science team.

The data science VM comes with several popular tools pre-installed like Revolution R Open, Anaconda Python distribution including Jupyter notebook server, Visual Studio Community Edition, Power BI Desktop, SQL Server Express edition and Azure SDK. Once you provision your virtual machine from this image you can get started with data exploration and modeling right away. The data on the virtual machine is stored on the cloud and highly available. You have full administrative access to the virtual machine and can install additional software as needed. There is no separate software fee to use the VM image. You only pay for actual hardware compute usage of the virtual machine depending on the size of the virtual machine you are provisioning this VM on. You can turn off the machine from Azure portal when it is not in use to avoid being billed. When you restart the virtual machine from the Azure portal you can continue your development with all your data and files intact. Further augment your analytics on your data science virtual machine by leveraging solutions in Microsoft’s Cortana Analytics Suite.

The data science virtual machine helps you create an analytics environment where you can rapidly build advanced analytics solutions for deployment to the cloud, on-premises or in a hybrid environment.

We are happy to announce that we have released Azure ML in our Western Europe datacenter (Amsterdam). Now you can create workspaces in this datacenter. For more information, click here: http://aka.ms/mlwelaunch.

We are happy to announce that we have released Azure ML in our SouthEast Asia datacenter (Singapore). Now you can create workspaces in this datacenter. For more information, click here: http://aka.ms/mlasialaunch.

A free Excel add-in that you can use with web services published from Azure Machine Learning is now available. You can use this add-in for request/response predictions or batch predictions, work in Windows or the browser, share workbooks with your co-workers, and call multiple web services all within a single spreadsheet. Go to http://aka.ms/amlexcelhelp for help or ask a question here.

To try it out, open and download sample Excel worksheets that already contain web services:

On July 24th, 2015, Microsoft announced the Preview Availability release of Jupyter Notebooks in Azure Machine Learning Studio.

Azure Machine Learning Studio is a powerful canvas for the composition of Machine Learning Experiments and subsequent operationalization and consumption. It provides an easy to use, yet powerful, drag-drop style of creating Experiments. But sometimes you need a good old “REPL” that allows you to have a tight loop where you enter some script code and get a response. We are delighted to announce that we’ve now integrated this functionality into ML Studio through Jupyter Notebooks.

Jupyter enables the concept of “executable documents” with support for mixed code, markdown and inline graphics. It’s one of the most important innovations in the Data Science and Technical Computing space in recent years. You now have full access to its power from any OS, from any modern browser directly from inside the Azure Machine Learning Studio.

In addition to authoring capabilities above, we are also enabling publishing AzureML web services directly from the Jupyter Notebook. We are also extending this capability to the Jupyter Notebooks running locally outside of AzureML Studio. This allows you to publish any function, including those creating ML models, to be published as a web service directly from the Jupyter Notebook running on your machine. The result is an AzureML web service API that can be called to perform functions or predictions from client applications in real time and over the internet.

We have
released support for Azure Document DB as a data source in Azure Machine
Learning. You can use the existing "Azure DocumentDB" connection
option in the Import Data module to read data from Azure DocumentDB for your experiment.
For more information, please see the DocumentDB
section of the Import Data module.

You can use this module to extract key talking points from text. As an input, the module takes a dataset that must have a text string column from which the key-phrases are extracted.

The module takes the language of the text records as input parameter. Supported languages include Dutch, English, French, German, Italian and Spanish. You can also use a language column that specifies the language of each record, as produced by Detect Languages module.

The output consists of comma-separated lists of key phrases for each record in input. The key phrases can be used to summarize a corpus of documents, or as features for a machine learning model.

Updated Module: Preprocess Text

You can specify a language through a language column, as produced by Detect Languages module.

Following three preprocessing options have been added: Expand verb contractions, Normalize backslashes to slashes, and Split tokens on special characters. Previously, these transformations were done automatically.

We are pleased to announce the availability of Azure Machine Learning Workspaces and Web Service Plans for all our Azure Machine Learning users through the Azure Portal. Azure Machine Learning users can now create and manage Standard workspaces through the Azure Portal. In addition, users will also be able to create Web Service Pricing Plans. These plans are used when deploying web services and provide included quantities of operationalized compute at a single, predictable monthly cost.

Create your Standard Azure Machine Learning workspace now by going to https://portal.azure.com. Log in with the credentials that you use for accessing your Azure Subscription(s). Click on +New | Data + Analytics | Machine Learning Workspace.

These modules allow you to build models to solve text classification problems, such as support ticket routing or sentiment analysis. You can pre-process text in multiple languages, and then create features from your text data. Operationalization of models is fully supported.

The modules complement the existing capabilities for Feature Hashing, Vowpal Wabbit based high-dimensional models, and text analytics through R and Python scripting.

There is an issue impacting the "New" web service option for deploying web services from Predictive Experiments in Azure ML. We are working on resolving the issue, and a result have disabled the feature until the feature is fully functional. To access web services created the new process, please browse to https://services.azureml.net and sign in to view your web services. Sorry for any inconvenience this issue may cause.

We have released support for Azure SQL Data
Warehouse as a data source and a destination in Azure Machine Learning. You can
use the existing "Azure SQL Database" connection options in the
Reader and Writer modules to read from and write to Azure SQL Data Warehouse.
When using the Writer module, the destination tables must already exist in the
SQL Data Warehouse.

Visualization of tree models such as Boosted Decision Trees is now available in Azure Machine Learning Studio. To view the trees, train the model, and click Visualize on the output of Train Model module.

Announcing the Availability of an Azure Virtual Machine Image with Popular Data Science Tools

Microsoft Data Group is happy to announce the immediate availability of a Windows Server 2012 based custom virtual machine image on the Azure marketplace containing several tools that can be used by data scientists and developers for advanced analytics. Through Azure’s world-wide cloud infrastructure, customers now have on-demand access to a data science development environment they can use to derive insights from their data, build predictive models and intelligent applications.The virtual machine saves developers’ time from having to discover and install the tools individually.Hosting the data science machine on Azure gains you high availability and a consistent set of tools used across your data science team.

The data science VM comes with several popular tools pre-installed like Revolution R Open, Anaconda Python distribution including Jupyter notebook server, Visual Studio Community Edition, Power BI Desktop, SQL Server Express edition and Azure SDK. Once you provision your virtual machine from this image you can get started with data exploration and modeling right away. The data on the virtual machine is stored on the cloud and highly available. You have full administrative access to the virtual machine and can install additional software as needed. There is no separate software fee to use the VM image. You only pay for actual hardware compute usage of the virtual machine depending on the size of the virtual machine you are provisioning this VM on. You can turn off the machine from Azure portal when it is not in use to avoid being billed. When you restart the virtual machine from the Azure portal you can continue your development with all your data and files intact. Further augment your analytics on your data science virtual machine by leveraging solutions in Microsoft’s Cortana Analytics Suite.

The data science virtual machine helps you create an analytics environment where you can rapidly build advanced analytics solutions for deployment to the cloud, on-premises or in a hybrid environment.

We are happy to announce that we have released Azure ML in our Western Europe datacenter (Amsterdam). Now you can create workspaces in this datacenter. For more information, click here: http://aka.ms/mlwelaunch.

We are happy to announce that we have released Azure ML in our SouthEast Asia datacenter (Singapore). Now you can create workspaces in this datacenter. For more information, click here: http://aka.ms/mlasialaunch.

A free Excel add-in that you can use with web services published from Azure Machine Learning is now available. You can use this add-in for request/response predictions or batch predictions, work in Windows or the browser, share workbooks with your co-workers, and call multiple web services all within a single spreadsheet. Go to http://aka.ms/amlexcelhelp for help or ask a question here.

To try it out, open and download sample Excel worksheets that already contain web services:

On July 24th, 2015, Microsoft announced the Preview Availability release of Jupyter Notebooks in Azure Machine Learning Studio.

Azure Machine Learning Studio is a powerful canvas for the composition of Machine Learning Experiments and subsequent operationalization and consumption. It provides an easy to use, yet powerful, drag-drop style of creating Experiments. But sometimes you need a good old “REPL” that allows you to have a tight loop where you enter some script code and get a response. We are delighted to announce that we’ve now integrated this functionality into ML Studio through Jupyter Notebooks.

Jupyter enables the concept of “executable documents” with support for mixed code, markdown and inline graphics. It’s one of the most important innovations in the Data Science and Technical Computing space in recent years. You now have full access to its power from any OS, from any modern browser directly from inside the Azure Machine Learning Studio.

In addition to authoring capabilities above, we are also enabling publishing AzureML web services directly from the Jupyter Notebook. We are also extending this capability to the Jupyter Notebooks running locally outside of AzureML Studio. This allows you to publish any function, including those creating ML models, to be published as a web service directly from the Jupyter Notebook running on your machine. The result is an AzureML web service API that can be called to perform functions or predictions from client applications in real time and over the internet.